Scott Marek is assistant professor of radiology in the Mallinckrodt Institute of Radiology at Washington University School of Medicine in St. Louis. Marek received a Ph.D. in neuroscience from the University of Pittsburgh, where he gained expertise in pediatric neuroimaging with Beatriz Luna. Subsequently, he completed a postdoctoral fellowship with Nico Dosenbach at Washington University School of Medicine, where he gained expertise in functional mapping of individual brains and leveraging big data to quantify the reproducibility of brain-wide association studies. He now runs his own lab focused on precision imaging and deep phenotyping of adolescent twins with depression, as well as population neuroscience approaches using large datasets, such as the Adolescent Brain Cognitive Development (ABCD) Study.
Scott Marek
Assistant professor of radiology
Washington University School of Medicine in St. Louis
From this contributor
Breaking down the winner’s curse: Lessons from brain-wide association studies
We found an issue with a specific type of brain imaging study and tried to share it with the field. Then the backlash began.
Breaking down the winner’s curse: Lessons from brain-wide association studies
Explore more from The Transmitter
Leucovorin saga, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 15 June.
Leucovorin saga, and more
Here is a roundup of autism-related news and research spotted around the web for the week of 15 June.
Models at the speed of thought: How AI coding is reshaping theoretical neuroscience
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Six neuroscientists weigh in on what that tectonic change may bring to the field.
Models at the speed of thought: How AI coding is reshaping theoretical neuroscience
Agentic coding makes it possible to specify a neuroscience model in hours instead of months. Six neuroscientists weigh in on what that tectonic change may bring to the field.
Writing science that humans and machines can read
Large language models are now routinely used to search, summarize and synthesize the literature at scales impossible for any individual researcher—yet scientific publishing has not adapted to that reality.
Writing science that humans and machines can read
Large language models are now routinely used to search, summarize and synthesize the literature at scales impossible for any individual researcher—yet scientific publishing has not adapted to that reality.